The Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Patients with Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
Background: Metastatic renal cell carcinoma (mRCC) is an aggressive cancer characterised by an increased recurrence rate and an inadequate response to treatment. This study aimed to investigate the importance of the neutrophil-to-lymphocyte ratio (NLR) as a prognostic marker for long-term survival in patients with mRCC. Methods: We retrospectively analysed data from 74 patients with mRCC treated at our medical centre with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). We evaluated the predictive value of NLR for overall survival (OS) in these patients. Results: The median OS was 5.1 months in the higher NLR group (≥3) and 13.3 months in the lower NLR group (<3) (p < 0.0001). There was no significant difference in the OS between the TKI and ICI therapies in the low NLR group (12.9 vs. 13.6 months, p = 0.411) or in the high NLR group (4.7 vs. 5.5 months, p = 0.32). Both univariate and multivariate analyses revealed that a higher NLR was an independent prognostic factor of long-term survival in patients with mRCC treated with first-line therapy. Conclusions: This retrospective study showed that adding NLR to other Memorial Sloan Kettering Cancer Center (MSKCC) and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) variables might improve the prognostic and predictive power of these models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".